Overview

Dataset statistics

Number of variables14
Number of observations49894
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.3 MiB
Average record size in memory112.0 B

Variable types

Numeric9
DateTime2
Categorical2
Boolean1

Alerts

staff_id is highly overall correlated with sales_outlet_idHigh correlation
customer_id is highly overall correlated with sales_outlet_idHigh correlation
line_item_id is highly overall correlated with product_idHigh correlation
product_id is highly overall correlated with line_item_idHigh correlation
quantity is highly overall correlated with line_item_amountHigh correlation
line_item_amount is highly overall correlated with quantity and 1 other fieldsHigh correlation
unit_price is highly overall correlated with line_item_amountHigh correlation
sales_outlet_id is highly overall correlated with staff_id and 1 other fieldsHigh correlation
promo_item_yn is highly imbalanced (92.0%)Imbalance
line_item_amount is highly skewed (γ1 = 42.89584499)Skewed
customer_id has 25033 (50.2%) zerosZeros

Reproduction

Analysis started2023-08-02 23:57:27.386211
Analysis finished2023-08-02 23:58:13.176663
Duration45.79 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

transaction_id
Real number (ℝ)

Distinct4203
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean869.05606
Minimum1
Maximum4203
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size389.9 KiB
2023-08-02T23:58:13.366846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile45
Q1223
median481
Q31401
95-th percentile2487.35
Maximum4203
Range4202
Interquartile range (IQR)1178

Descriptive statistics

Standard deviation857.86315
Coefficient of variation (CV)0.98712061
Kurtosis1.3597398
Mean869.05606
Median Absolute Deviation (MAD)372
Skewness1.3185563
Sum43360683
Variance735929.18
MonotonicityNot monotonic
2023-08-02T23:58:13.664029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
97 67
 
0.1%
49 66
 
0.1%
45 65
 
0.1%
274 65
 
0.1%
252 64
 
0.1%
319 64
 
0.1%
179 64
 
0.1%
36 63
 
0.1%
177 63
 
0.1%
324 63
 
0.1%
Other values (4193) 49250
98.7%
ValueCountFrequency (%)
1 57
0.1%
2 54
0.1%
3 59
0.1%
4 54
0.1%
5 62
0.1%
6 56
0.1%
7 55
0.1%
8 56
0.1%
9 59
0.1%
10 57
0.1%
ValueCountFrequency (%)
4203 1
< 0.1%
4202 1
< 0.1%
4201 1
< 0.1%
4200 1
< 0.1%
4199 1
< 0.1%
4198 1
< 0.1%
4197 1
< 0.1%
4196 1
< 0.1%
4195 2
< 0.1%
4194 1
< 0.1%
Distinct29
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size389.9 KiB
Minimum2019-04-01 00:00:00
Maximum2019-04-29 00:00:00
2023-08-02T23:58:13.949639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:58:14.217246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
Distinct26074
Distinct (%)52.3%
Missing0
Missing (%)0.0%
Memory size389.9 KiB
Minimum2023-08-02 01:00:44
Maximum2023-08-02 20:59:32
2023-08-02T23:58:14.487963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:58:14.784381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

sales_outlet_id
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size389.9 KiB
8
17071 
3
16829 
5
15994 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49894
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
8 17071
34.2%
3 16829
33.7%
5 15994
32.1%

Length

2023-08-02T23:58:15.084108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-02T23:58:15.340112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
8 17071
34.2%
3 16829
33.7%
5 15994
32.1%

Most occurring characters

ValueCountFrequency (%)
8 17071
34.2%
3 16829
33.7%
5 15994
32.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49894
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 17071
34.2%
3 16829
33.7%
5 15994
32.1%

Most occurring scripts

ValueCountFrequency (%)
Common 49894
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 17071
34.2%
3 16829
33.7%
5 15994
32.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49894
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 17071
34.2%
3 16829
33.7%
5 15994
32.1%

staff_id
Real number (ℝ)

Distinct25
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.359582
Minimum6
Maximum45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size389.9 KiB
2023-08-02T23:58:15.557703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile8
Q115
median26
Q341
95-th percentile45
Maximum45
Range39
Interquartile range (IQR)26

Descriptive statistics

Standard deviation12.46649
Coefficient of variation (CV)0.49158894
Kurtosis-1.2641419
Mean25.359582
Median Absolute Deviation (MAD)12
Skewness0.34018302
Sum1265291
Variance155.41338
MonotonicityNot monotonic
2023-08-02T23:58:15.878648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
12 5930
 
11.9%
42 4085
 
8.2%
45 3648
 
7.3%
26 3629
 
7.3%
16 3198
 
6.4%
20 3127
 
6.3%
17 3059
 
6.1%
14 2770
 
5.6%
30 2430
 
4.9%
44 2349
 
4.7%
Other values (15) 15669
31.4%
ValueCountFrequency (%)
6 1629
 
3.3%
7 633
 
1.3%
8 306
 
0.6%
9 380
 
0.8%
10 194
 
0.4%
12 5930
11.9%
13 90
 
0.2%
14 2770
5.6%
15 2034
 
4.1%
16 3198
6.4%
ValueCountFrequency (%)
45 3648
7.3%
44 2349
4.7%
43 2140
4.3%
42 4085
8.2%
41 1204
 
2.4%
30 2430
4.9%
29 2112
4.2%
28 1751
3.5%
27 2063
4.1%
26 3629
7.3%

customer_id
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2248
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2282.3245
Minimum0
Maximum8501
Zeros25033
Zeros (%)50.2%
Negative0
Negative (%)0.0%
Memory size389.9 KiB
2023-08-02T23:58:16.317624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35412
95-th percentile8342
Maximum8501
Range8501
Interquartile range (IQR)5412

Descriptive statistics

Standard deviation3240.5518
Coefficient of variation (CV)1.4198471
Kurtosis-0.90040729
Mean2282.3245
Median Absolute Deviation (MAD)0
Skewness0.92450401
Sum1.138743 × 108
Variance10501176
MonotonicityNot monotonic
2023-08-02T23:58:16.834059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 25033
50.2%
3 31
 
0.1%
548 31
 
0.1%
8285 31
 
0.1%
8341 29
 
0.1%
8009 28
 
0.1%
8118 28
 
0.1%
8036 28
 
0.1%
8158 28
 
0.1%
8101 27
 
0.1%
Other values (2238) 24600
49.3%
ValueCountFrequency (%)
0 25033
50.2%
1 8
 
< 0.1%
2 21
 
< 0.1%
3 31
 
0.1%
4 9
 
< 0.1%
5 6
 
< 0.1%
6 12
 
< 0.1%
7 11
 
< 0.1%
8 13
 
< 0.1%
9 12
 
< 0.1%
ValueCountFrequency (%)
8501 6
 
< 0.1%
8500 21
< 0.1%
8499 12
< 0.1%
8498 9
< 0.1%
8497 12
< 0.1%
8496 14
< 0.1%
8495 17
< 0.1%
8494 18
< 0.1%
8493 16
< 0.1%
8492 21
< 0.1%

instore_yn
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size389.9 KiB
Y
24992 
N
24608 
 
294

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49894
Distinct characters3
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowY
4th rowN
5th rowY

Common Values

ValueCountFrequency (%)
Y 24992
50.1%
N 24608
49.3%
294
 
0.6%

Length

2023-08-02T23:58:17.322073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-02T23:58:17.791411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
y 24992
50.4%
n 24608
49.6%

Most occurring characters

ValueCountFrequency (%)
Y 24992
50.1%
N 24608
49.3%
294
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 49600
99.4%
Space Separator 294
 
0.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Y 24992
50.4%
N 24608
49.6%
Space Separator
ValueCountFrequency (%)
294
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 49600
99.4%
Common 294
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
Y 24992
50.4%
N 24608
49.6%
Common
ValueCountFrequency (%)
294
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49894
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Y 24992
50.1%
N 24608
49.3%
294
 
0.6%

order
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1734277
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size389.9 KiB
2023-08-02T23:58:18.618045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0254447
Coefficient of variation (CV)0.87388832
Kurtosis35.05642
Mean1.1734277
Median Absolute Deviation (MAD)0
Skewness5.9891027
Sum58547
Variance1.0515369
MonotonicityNot monotonic
2023-08-02T23:58:19.094296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 48387
97.0%
7 916
 
1.8%
9 253
 
0.5%
6 132
 
0.3%
2 83
 
0.2%
5 61
 
0.1%
3 56
 
0.1%
8 4
 
< 0.1%
4 2
 
< 0.1%
ValueCountFrequency (%)
1 48387
97.0%
2 83
 
0.2%
3 56
 
0.1%
4 2
 
< 0.1%
5 61
 
0.1%
6 132
 
0.3%
7 916
 
1.8%
8 4
 
< 0.1%
9 253
 
0.5%
ValueCountFrequency (%)
9 253
 
0.5%
8 4
 
< 0.1%
7 916
 
1.8%
6 132
 
0.3%
5 61
 
0.1%
4 2
 
< 0.1%
3 56
 
0.1%
2 83
 
0.2%
1 48387
97.0%

line_item_id
Real number (ℝ)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6318595
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size389.9 KiB
2023-08-02T23:58:19.591673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile5
Maximum12
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.4128807
Coefficient of variation (CV)0.86581024
Kurtosis2.6969449
Mean1.6318595
Median Absolute Deviation (MAD)0
Skewness2.0433121
Sum81420
Variance1.9962319
MonotonicityNot monotonic
2023-08-02T23:58:20.074942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 39949
80.1%
5 6332
 
12.7%
2 2547
 
5.1%
3 503
 
1.0%
6 324
 
0.6%
4 177
 
0.4%
9 55
 
0.1%
8 4
 
< 0.1%
12 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
1 39949
80.1%
2 2547
 
5.1%
3 503
 
1.0%
4 177
 
0.4%
5 6332
 
12.7%
6 324
 
0.6%
7 1
 
< 0.1%
8 4
 
< 0.1%
9 55
 
0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
12 1
 
< 0.1%
10 1
 
< 0.1%
9 55
 
0.1%
8 4
 
< 0.1%
7 1
 
< 0.1%
6 324
 
0.6%
5 6332
12.7%
4 177
 
0.4%
3 503
 
1.0%
2 2547
5.1%

product_id
Real number (ℝ)

Distinct80
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.878983
Minimum1
Maximum87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size389.9 KiB
2023-08-02T23:58:20.622359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile23
Q133
median47
Q360
95-th percentile78
Maximum87
Range86
Interquartile range (IQR)27

Descriptive statistics

Standard deviation17.928355
Coefficient of variation (CV)0.37445146
Kurtosis-0.67144817
Mean47.878983
Median Absolute Deviation (MAD)13
Skewness0.19720739
Sum2388874
Variance321.42592
MonotonicityNot monotonic
2023-08-02T23:58:21.303865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71 1034
 
2.1%
50 1015
 
2.0%
59 1000
 
2.0%
61 995
 
2.0%
38 993
 
2.0%
29 991
 
2.0%
52 985
 
2.0%
54 984
 
2.0%
26 978
 
2.0%
32 978
 
2.0%
Other values (70) 39941
80.1%
ValueCountFrequency (%)
1 69
0.1%
2 65
0.1%
3 53
0.1%
4 47
0.1%
5 50
0.1%
6 70
0.1%
7 51
0.1%
8 67
0.1%
9 67
0.1%
10 48
0.1%
ValueCountFrequency (%)
87 755
1.5%
84 569
1.1%
83 97
 
0.2%
82 69
 
0.1%
81 79
 
0.2%
79 677
1.4%
78 639
1.3%
77 611
1.2%
76 622
1.2%
75 645
1.3%

quantity
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.438209
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size389.9 KiB
2023-08-02T23:58:21.772562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile2
Maximum8
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5430387
Coefficient of variation (CV)0.37757983
Kurtosis1.0970904
Mean1.438209
Median Absolute Deviation (MAD)0
Skewness0.82201515
Sum71758
Variance0.29489103
MonotonicityNot monotonic
2023-08-02T23:58:22.201125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 29170
58.5%
2 19616
39.3%
3 1094
 
2.2%
4 9
 
< 0.1%
8 4
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
1 29170
58.5%
2 19616
39.3%
3 1094
 
2.2%
4 9
 
< 0.1%
6 1
 
< 0.1%
8 4
 
< 0.1%
ValueCountFrequency (%)
8 4
 
< 0.1%
6 1
 
< 0.1%
4 9
 
< 0.1%
3 1094
 
2.2%
2 19616
39.3%
1 29170
58.5%

line_item_amount
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct77
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6826462
Minimum0
Maximum360
Zeros24
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size389.9 KiB
2023-08-02T23:58:22.603332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median3.75
Q36
95-th percentile9
Maximum360
Range360
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.4366683
Coefficient of variation (CV)0.94747031
Kurtosis3308.2068
Mean4.6826462
Median Absolute Deviation (MAD)1.25
Skewness42.895845
Sum233635.95
Variance19.684025
MonotonicityNot monotonic
2023-08-02T23:58:23.054425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 5916
 
11.9%
6 5149
 
10.3%
2.5 4376
 
8.8%
5 4120
 
8.3%
3.75 4109
 
8.2%
3.5 3184
 
6.4%
7.5 2119
 
4.2%
3.25 1992
 
4.0%
4 1384
 
2.8%
7 1357
 
2.7%
Other values (67) 16188
32.4%
ValueCountFrequency (%)
0 24
 
< 0.1%
0.8 1080
 
2.2%
1 156
 
0.3%
1.6 1130
 
2.3%
2 929
 
1.9%
2.1 184
 
0.4%
2.2 913
 
1.8%
2.4 31
 
0.1%
2.45 435
 
0.9%
2.5 4376
8.8%
ValueCountFrequency (%)
360 4
 
< 0.1%
72 1
 
< 0.1%
56 1
 
< 0.1%
45 63
0.1%
36 1
 
< 0.1%
28 84
0.2%
24 1
 
< 0.1%
23 12
 
< 0.1%
22.5 55
0.1%
21 70
0.1%

unit_price
Real number (ℝ)

Distinct41
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3846454
Minimum0.8
Maximum45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size389.9 KiB
2023-08-02T23:58:23.624043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.8
5-th percentile2
Q12.5
median3
Q33.75
95-th percentile4.5
Maximum45
Range44.2
Interquartile range (IQR)1.25

Descriptive statistics

Standard deviation2.6825447
Coefficient of variation (CV)0.79256299
Kurtosis99.83137
Mean3.3846454
Median Absolute Deviation (MAD)0.5
Skewness8.5307304
Sum168873.5
Variance7.1960461
MonotonicityNot monotonic
2023-08-02T23:58:24.137571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
3 11316
22.7%
2.5 8762
17.6%
3.75 6214
12.5%
3.5 4609
9.2%
0.8 2248
 
4.5%
3.25 2023
 
4.1%
2.2 1904
 
3.8%
4.25 1903
 
3.8%
2 1877
 
3.8%
3.1 1862
 
3.7%
Other values (31) 7176
14.4%
ValueCountFrequency (%)
0.8 2248
 
4.5%
2 1877
 
3.8%
2.1 256
 
0.5%
2.2 1904
 
3.8%
2.45 883
 
1.8%
2.5 8762
17.6%
2.55 907
 
1.8%
2.65 96
 
0.2%
3 11316
22.7%
3.1 1862
 
3.7%
ValueCountFrequency (%)
45 67
0.1%
28 83
0.2%
23 12
 
< 0.1%
22.5 55
0.1%
21 70
0.1%
20.45 47
 
0.1%
19.75 51
 
0.1%
18 134
0.3%
15 50
 
0.1%
14.75 53
 
0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size48.8 KiB
False
49404 
True
 
490
ValueCountFrequency (%)
False 49404
99.0%
True 490
 
1.0%
2023-08-02T23:58:24.637396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Interactions

2023-08-02T23:58:09.670163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:57:31.196478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:57:36.989267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:57:42.128876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:57:46.162881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:57:51.725488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:57:57.540243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:58:02.186658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:58:05.825829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:58:10.044638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:57:31.671345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:57:37.756279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:57:42.592303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:57:46.551743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:57:52.661756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:57:57.996583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:58:02.626114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:58:06.244982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:58:10.317351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:57:32.135352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:57:38.608902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:57:43.120583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:57:46.975793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:57:53.965707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:57:58.516550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:58:03.117451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:58:06.694040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:58:10.564706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:57:32.538565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:57:39.085077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:57:43.469156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:57:47.499930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:57:54.752124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:57:58.924123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:58:03.582921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:58:07.041720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:58:10.839898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:57:32.884351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:57:39.638492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:57:43.876474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:57:47.945524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:57:55.358315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:57:59.604764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:58:03.881633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:58:07.492956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:58:11.094819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:57:33.335973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:57:40.031974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:57:44.317727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:57:48.487890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:57:55.923064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:58:00.125918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:58:04.129167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:58:07.916399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:58:11.332311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:57:34.161207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:57:40.509543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:57:45.021124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:57:49.154266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:57:56.401159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:58:00.636738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:58:04.648061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:58:08.334500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:58:11.596014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:57:35.267761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:57:41.069469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:57:45.455881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:57:50.047825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:57:56.826693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:58:01.314771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:58:04.981984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:58:08.797918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:58:11.871617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:57:36.209405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:57:41.602814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:57:45.794561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:57:50.824065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:57:57.167378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:58:01.841700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:58:05.427714image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T23:58:09.224083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-08-02T23:58:24.985986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
transaction_idstaff_idcustomer_idorderline_item_idproduct_idquantityline_item_amountunit_pricesales_outlet_idinstore_ynpromo_item_yn
transaction_id1.0000.0020.042-0.061-0.071-0.0430.0210.021-0.0070.2020.0480.036
staff_id0.0021.0000.1120.0070.0010.0090.0110.001-0.0110.8600.1290.073
customer_id0.0420.1121.000-0.034-0.030-0.0120.0090.012-0.0010.6090.0250.015
order-0.0610.007-0.0341.000-0.020-0.154-0.1410.2450.2740.0630.0050.012
line_item_id-0.0710.001-0.030-0.0201.0000.628-0.301-0.2540.1230.0810.1350.235
product_id-0.0430.009-0.012-0.1540.6281.000-0.175-0.1260.2160.0640.0080.261
quantity0.0210.0110.009-0.141-0.301-0.1751.0000.682-0.1590.1550.0080.035
line_item_amount0.0210.0010.0120.245-0.254-0.1260.6821.0000.5220.0070.0000.000
unit_price-0.007-0.011-0.0010.2740.1230.216-0.1590.5221.0000.0270.0120.012
sales_outlet_id0.2020.8600.6090.0630.0810.0640.1550.0070.0271.0000.0750.077
instore_yn0.0480.1290.0250.0050.1350.0080.0080.0000.0120.0751.0000.004
promo_item_yn0.0360.0730.0150.0120.2350.2610.0350.0000.0120.0770.0041.000

Missing values

2023-08-02T23:58:12.254302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-02T23:58:12.843661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

transaction_idtransaction_datetransaction_timesales_outlet_idstaff_idcustomer_idinstore_ynorderline_item_idproduct_idquantityline_item_amountunit_pricepromo_item_yn
072019-04-012023-08-02 12:04:43312558N115212.502.50N
1112019-04-012023-08-02 15:54:39317781N112727.003.50N
2192019-04-012023-08-02 14:34:59317788Y114625.002.50N
3322019-04-012023-08-02 16:06:04312683N112325.002.50N
4332019-04-012023-08-02 19:18:3731799Y113412.452.45N
5392019-04-012023-08-02 18:54:46317664Y113213.003.00N
6502019-04-012023-08-02 13:03:49312316N114926.003.00N
7532019-04-012023-08-02 11:21:1431238N116013.753.75N
8592019-04-012023-08-02 19:30:55312370Y115126.003.00N
9622019-04-012023-08-02 12:01:00312180Y114926.003.00N
transaction_idtransaction_datetransaction_timesales_outlet_idstaff_idcustomer_idinstore_ynorderline_item_idproduct_idquantityline_item_amountunit_pricepromo_item_yn
498847422019-04-292023-08-02 19:23:258450N157413.503.50N
498857462019-04-292023-08-02 15:54:118150N113726.003.00N
498867462019-04-292023-08-02 15:54:118150N157113.753.75N
498877492019-04-292023-08-02 19:34:508150N114713.003.00N
498887522019-04-292023-08-02 16:36:248428401N118712.102.10Y
498897532019-04-292023-08-02 16:51:588420N113013.003.00N
498907562019-04-292023-08-02 16:51:148428412Y112524.402.20N
498917592019-04-292023-08-02 11:17:368150Y113112.202.20N
498927632019-04-292023-08-02 15:45:528458030N114425.002.50N
498937632019-04-292023-08-02 15:45:528458030N157513.503.50N